Annualized Life Cycle Cost Research Articles

Techno-economic assessment of an industrial prosumer with biomass investment and time varying tariffs: An Australian case study

This paper investigates the potential for a value chain framework to deliver impact through innovation across the timber manufacturing process. A new and efficient combustion technology that converts timber waste to energy is considered for this study. The framework to estimate the new energy costs and savings derived from the new technology, compared with current supply and demand scenarios, as well as the value generated by waste streams. The opportunity of selling excess energy to the grid or local area has been investigated. Two alternatives of time-varying tariffs and time-varying tariffs with biomass, are used for assessing the costs. According to the numerical results, tariff 3, with 25,000 tonnes of biomass feedstock per year, is the best option for the mill. The price efficiency index is reduced by approximately 40% compared to this option’s usual business. In addition, the investor can save the whole energy bill compared to the current business as usual. The investor could make a profit of $460,401 per year by selling energy to the grid. The annual saving is around six times higher than the savings gained using a time-varying tariff alone. However, this option requires $1,811,635 as annual life cycle cost, with a payback period of ten years. The lowest levelised cost of energy of 0.14 c/kWh is also obtained for this option.

Powering sustainability: A techno-economic analysis of photovoltaic systems in dairy farming

ABSTRACT Dairy farming plays an indispensable role in the socio-economic growth of rural India. But, the modernization and sustainable growth of dairy farming relies critically upon the availability of reliable and affordable renewable electric energy. However, lack of access to electricity is a major challenge that needs to be addressed to encourage sustainable dairy farming. In this direction, the deployment of solar photovoltaic (PV) systems in dairy farms has enormous potential to combat the existing critical challenges of fossil fuels and rural energy crises. This investigation assesses the techno-economic viability of PV systems through detailed mathematical modeling and electrical load estimation using the MATLAB simulation environment. The study determines the availability of maximum of solar radiations at each instant, and optimizes the inclined surface to extract maximum solar power. The outcomes of the study reveal that deploying an off-grid PV system consisting of 20 PV modules of 335 Wp, 24 batteries of each 150 Ah, and a 10 kVA inverter meets the average electric load of 9.5 MWh/year for twenty-five years with one-day autonomy. The economic analysis comprised the life-cycle cost (LCC), annualized life-cycle cost (ALCC), and costs of electricity (COE) of the study under reference as US$13050, US$323.1, and US$ 0.02596/kWh, respectively. The study results in saving of 0.285 MT CO2 emissions for the total life span. The resulted stand-alone PV system is both economically and technically viable. The present results reinforce the dispersal of green energy measures for their implementation in rural dairy farms having similar climate conditions in the Indian context.

Enhanced resilience in urban stormwater management through model predictive control and optimal layout schemes under extreme rainfall events

Optimizing the layout of urban stormwater management systems is an effective method for mitigating the risk of urban flooding under extreme storms. However, traditional approaches that consider only economic costs or annual runoff control rates cannot dynamically respond to the uncertainties of extreme weather, making it difficult to completely avoid large accumulations of water and flooding in a short period. This study proposes an integrated method combining system layout optimization and Model Predictive Control(MPC)to enhance the system's resilience and effectiveness in flood control. An optimization framework was initially built to identify optimal system layouts, balancing annual average life cycle cost (AALCC) and resilience index. The MPC was then applied to the optimal layout selected using the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method, aiming to alleviate inundation cost-effectively. The adaptability of MPC to varying sets of control horizons and its efficacy in managing the hydrograph and flood dynamics of urban drainage system were examined. Conducted in Yubei, Chongqing, this study revealed patterns in optimal layout fronts among various extreme design rainfalls, showing that peak position rate and return period significantly influence system resilience. The contribution of MPC to the optimal system layout was particularly notable, resulting in improved instantaneous and overall flood mitigation. The application of MPC increased the resilience index by an average of 0.0485 and offered cost savings of 0.0514 million yuan in AALCC. Besides, our findings highlighted the importance of selecting an optimal set of control horizons for MPC, which could reduce maximum flood depth from 0.43m to 0.19m and decrease conduit peak flow by up to 14% at a flood-prone downstream location.

Configuration method for medium-deep ground source heat pump system considering renewable energy consumption and smart grid interaction

Medium-deep ground source heat pump (GSHP) systems have the advantages of low carbon, high heat exchange intensity, good thermal storage capacity, and intermittent operation characteristics, which are conducive to renewable energy consumption and grid demand response. The source-side parameters affect the configuration of underground borehole engineering, heat pump units, and other equipment. However, current research mainly focuses on the underground heat exchanger modeling, underground heat exchange, and system operation control, while there are few studies on multi-factor ground source-side parameter design and optimal configuration. In this study, the indicator analyzing the unsteady features and thermal balance state of medium-deep geothermal resources was introduced, and the source-side water temperatures were optimized based on the dynamic characteristics of the geothermal and building sides. A simulation configuration methodology for the source-side flow rate and storage capacity was developed considering the electricity prices, life-cycle cost (LCC), and grid interaction. The optimization of the ground source-side design temperature, flow rate, and capacity parameters of residential and office building heating scenarios were analyzed in northern China, respectively. The results showed that the borehole inlet and outlet temperature can be designed to 5.5 °C/17.5 °C under the annual sustainable state, and the optimal flowrate can be set to 65.77 m3/h, to achieve the lowest cost and highest heating season guarantee rate for the residential scenario. For the office building with optimal system and storage capacity, the average annual LCC is 192,000 yuan/year, with the cumulative peak shaving of 55.8 %, and an increase of 103.3MWh renewable electricity consumption.

Techno-Economic Analysis of Off-Grid PV Solar System for Residential Building Load: A Case Study in Baidoa, Somalia

Currently, the majority of the Somali population does not have access to a regular source of power. The country does not have a national grid, relying on outdated, costly and inefficient diesel generators. The energy consumption in Somalia is dependent on firewood and charcoal, dependencies that rely on deforestation and desertification, which negatively influence the agricultural sector and also the environment. In this work, the potential of solar power in Somalia is assessed while estimating the cost of solar panels per household. This study aims to assess the cost, ecological and economic efficiency of the off-grid PV home system in residential buildings in Baidoa, Somalia. A stand-alone solar home system of 1.98kW PV capacity with battery backup is designed by using HOMER software. The daily primary load considered is 7.530 kWh, with a peak of the nominal power of 1.60 kW. The results show that renewable energy sources can replace conventional energy sources and that they would be a viable solution for generating electrical energy in residential houses in Baidoa with a reasonable investment. It was also found that the amount of power produced by solar panels is 7,400kWh/year. With an initial investment of $5580, the annualized life cycle cost of the system is $0.483, the payback period of initial investment is 2 years and 7 months period, and the net present cost (NPC) of the project is $18,684.

Economic optimization of exterior wall insulation in Chinese office buildings by coupling artificial neural network and genetic algorithm

Envelope insulation is an essential measure to reduce building energy consumption and is the focus of many building energy codes. In China, the current standard (GB 55015-2021) only specified limit values for the thermal conductivity (U) of the exterior walls, and it ignored variations in building characteristics and internal parameters. Therefore, it is difficult for architects to quickly determine the exterior wall U-value in building design and retrofit. To address this issue, the exterior wall U-values of office buildings in China were economically optimized by TRNSYS, ANN, and GA. To begin with, the effect of enhancing exterior wall insulation on the heating and cooling energy consumption of office buildings was investigated by TRNSYS software, taking into account variations in building characteristics. Moreover, the exterior wall U-values of office buildings were optimized by coupling ANN and GA. The results showed that the design values of the GB 55015-2021 did not achieve the optimal results in China, and the most economical exterior wall U-value was 0.24 W/(m2·K) in Harbin, 0.24 W/(m2·K) in Beijing, 0.42 W/(m2·K) in Changsha, 1.82 W/(m2·K) in Guangzhou, 0.66 W/(m2·K) in Kunming. Compared with the pre-optimization, the annual life cycle cost of building heating and cooling reduced by 0.96 % in Harbin, 2.07 % in Beijing, 1.44 % in Changsha, 1.68 % in Guangzhou, and 7.37 % in Kunming. In addition, variations in building parameters should be considered in the design phase of new buildings or in the renovation of old buildings, such as internal heat gain, which may lead to different optimal results.

Optimization of solar-air source heat pump heating system with phase change heat storage

The optimization of heating system has always been of much concern, among which the annual life cycle cost (ALCC) was widely used as an optimization method, while the non-guaranteed hours were often overlooked. This study proposed the elasticity-economy (E-E) objective function as an optimizing indicator, and demonstrated it taking a solar assisted heat pump heating system with phase change material (SC-ASHP-PCM) as an example. Firstly, the single variation impact was analyzed. After optimizing the thermal performance of sub-systems, the proportion of heat supplied by solar collector (SC) and phase change material (PCM) sub-systems increased by 5.4 % and 3.4 %, respectively. Subsequently, the Hooke-Jeeves algorithm was developed to optimize multi-factors, hence minimizing the E-E objective function. The optimal solution was achieved when the non-guaranteed rate was 5 % and the penalty factor was 150 CNY/h. Compared with the baseline situation, the system energy consumption (156.8 × 103 kW‧h) was reduced by 30 %, and the E-E objective function was reduced by 24 %. Furthermore, the minimum operating cost was 18.3 CNY/m2, saving up to 46 % compared to traditional heating systems. The method is generalized to obtain the optimal energy saving potential under different non-guaranteed hours, which provides guidance for the heating systems' design and operation.

The optimization and energy efficiency analysis of a multi-tank solar-assisted air source heat pump water heating system

This article undertakes an optimization study of a solar-assisted air source heat pump water heating system by harnessing the advantages inherent in a multiple water tank arrangement. The Hooke-Jeeves algorithm was adopted to optimize the system parameters. The optimization variables encompass pivotal parameters such as water tank volume, collector area, dip angle, azimuth angle and heat pump power, while the objective function is the annual value of life cycle cost. By research and analysis of the monthly power consumption, electricity cost, solar fraction, average coefficient of performance (COP) and annual value of carbon emissions of the single-tank, dual-tank, and triple-tank system, it is discovered that the triple-tank system exhibits superior performance in overall aspects. Compared to the pre-optimized version, it achieves an annual energy consumption reduction of approximately 3.9%, leading to a 22.5% decrease in electricity expenses. Furthermore, it brings a 5.2% reduction in the annual lifecycle cost and a 6.3% decrease in carbon emissions, while simultaneously improving exergy efficiency by 4.8%.

Economic Viability of Solar Irrigation Pumps: A Comparative Life Cycle Cost Analysis with Diesel and Electric Pumps

In the present scenario viability of an irrigation pump is of extreme significance to set up it and get the sought after output in a more economic and better way. In this study investigation has been done about the economic viability of solar irrigation pump against the diesel and electric irrigation pump through the life cycle cost analysis. In LCC analysis, capital cost, future maintenance cost and replacement cost are converted to present worth value considering inflation rate and discount rate during its total life cycle. Annualized life cycle cost (ALCC) of solar irrigation pump has been further compared with grid connected electric and diesel irrigation pump. The results revealed that the annualized life cycle cost (ALCC) of solar irrigation pump is lower than grid connected electric and diesel irrigation pump. E.g., The ALCC of solar irrigation pump was estimated at 12383/year and 15435/year for 3 HP and 5 HP pump respectively, which is significantly leave behind electric pumps, which boast ALCC figures of 61641/year and 82371/year for 3 HP and 5 HP pumps. In the same way, when related to diesel irrigation pumps with ALCC values of 85891/year and 136088/year for 3 HP and 5 HP pumps respectively, the economic superiority of solar irrigation pumps becomes abundantly clear. . It has also been observed that the capital cost for solar irrigation pump is higher than grid connected electric and diesel irrigation pump. However, the maintenance cost and replacement cost are lower in case of solar. The comparison shows that the solar irrigation pump was more viable than the connected electric and diesel irrigation pump.

Optimal design of energy-flexible distributed energy systems and the impacts of energy storage specifications under evolving time-of-use tariff in cooling-dominated regions

To utilize the energy flexibility of demand side participants for achieving the global carbon-neutrality target, distributed energy systems (DESs) with active energy storages have attracted increasing attention. Existing studies mainly address the optimal configuration of DESs for enhanced energy flexibility. But the impacts of the energy storage specifications for DESs are rarely investigated, particularly under the evolving electricity markets towards carbon neutrality. This study, therefore, investigates the optimal design of energy-flexible DESs in cooling-dominated regions and the impacts of the economic and technical parameters of active energy storages under the evolving ToU tariff. A two-stage optimization method is developed for the optimal design of energy-flexible DESs. A district in Hong Kong is chosen as a reference site to perform the design case studies and impact analysis. It is found that the ToU tariff has significant impacts on the optimal design of energy-flexible DESs. The increase of the peak-to-valley ratio generally can increase the life-cycle economic benefits of DES energy-flexibility investment. The annualized life cycle cost of the optimized energy-flexible DES is decreased by up to $ 0.31 million when the peak-to-valley ratio increases from 2 to 10. A high unit price of energy storages (i.e., higher than 210 $/kWh for electric energy storages and 60 $/kWh for cold energy storages) will make them not economical to be adopted under all ToU tariffs. The maximum discharging rate of cold storages and the four specification parameters (i.e., the maximum charging rate, maximum discharging rate, charging/discharging efficiency, and lower limit of state of charge) of electric energy storages have considerable impacts on the energy-flexible DES design, and therefore are recommended to be optimized in design.

Techno-Economic Feasibility Assessment for the promotion of Grid-Connected Rooftop PV Systems in Botswana: A Case Study

The main aim of the present study is to investigate the solar energy potential and evaluate the economic viability of a 5kW grid-connected rooftop photovoltaic (PV) system as an electricity generation source in three selected regions (Gaborone, Maun, and Tshabong) in Botswana for the first time. In this study, NASA POWER data were used for evaluating the solar potential in the selected regions. The results showed that the selected locations are suitable for the installation of various scales of PV systems due to the high global horizontal solar radiation. RETScreen Expert software was used to assess the techno-economic feasibility of the proposed systems. The performance of the proposed systems with various PV technologies (mono-crystalline silicon and poly-crystalline silicon) is analyzed. Furthermore, economic and financial indicators such as net present value, annual life cycle savings, payback, benefit-cost ratio, and cost of energy production were calculated. The results indicate that the proposed system is very promising for all the selected locations. Additionally, it was found that the PV projects with poly-Si technology produced a large amount of energy and have a low electricity cost compared to mono-Si technology. The results suggest that grid-connected rooftop PV systems have a significant role in covering the electricity demand and in reducing carbon dioxide emissions, especially in high population density and rural regions. This study provides some useful recommendations for decision-makers regarding the development and deployment of PV energy technology in Botswana.

Performance analysis of solar-assisted-geothermal combined cooling, heating, and power (CCHP) systems incorporated with a hydrogen generation subsystem

In this study, the response surface method (RSM) and transient assessment was used to evaluate the energy and economic performance of a solar-assisted-geothermal combined cooling, heating, and power system (SG-CCHP). The proposed SG-CCHP process consisted of two steam turbines (STs), photovoltaic/thermal (PV/T) collectors, a fuel cell circuit, an absorption chiller, and a heat pump (HP), with battery cells and a hydrogen storage container as the power storage modules. The system's performance was investigated using the TRNSYS modeling tool. The design of experiments (DOE) approach was used to determine the optimal arrangement of the SG-CCHP scheme by controlling the key design factors. A number of simulation scenarios were generated using DOE, and their outcomes were analyzed using RSM. The transient interactions of the controlling design factors on the techno-economic metrics were given after RSM identified the optimal SG-CCHP scheme. The number of PV/T panels, steam turbine capacity, fuel cell power, HP heating capacity and absorption chiller cooling capacity were considered as decision variables. Total electricity consumption (TEU) and auxiliary boiler fuel consumption (ABFU) as indicators of primary energy consumption of the system and predicted average vote (PMV) as an index of thermal comfort of the system and life cycle cost (LCC) as an economic measure to 4 objective functions were selected for optimization. The results indicated that the optimal system significantly reduced its annual life cycle costs, thermal comfort score, total power usage, and auxiliary boiler natural gas usage. The findings also demonstrated that the SG-CCHP system's integration of battery and hydrogen storage components achieved the maximum efficiencies of 90%, 60%, 23%, and 18% for the electrolyzer, fuel cell, PV/T solar collector, and electrical generator, respectively over a year. The optimization results showed that the system cycle cost (LCC) is $514,188.21 per year, the system comfort coefficient (PMV) is 0.257 per year, the boiler fuel consumption is 46,271.40 cubic meters per year, and the total electricity consumption is −50,082.37 kWh per year.

A Strategy for Determining the Decommissioning Life of Energy Equipment Based on Economic Factors and Operational Stability

LCC and EL models have been widely used in recent years to determine the decommissioning life of equipment in energy companies, with LCC (life-cycle cost) being the total “lifetime” cost of the equipment from the time it is put into operation until the end of its decommissioning and disposal; the average annual cost of the equipment can be calculated based on the LCC. The overall LCC can be calculated as the average annual LCC, while the EL is the age of the equipment at which its average annual LCC is the lowest. It is believed that the decommissioning of the equipment in the EL year will result in the lowest annual average equipment turnover, thus maximizing the economic benefits of the equipment. Recently, LCC and EL research has been gradually introduced to the energy field, but there remains a lack of research depth. In current practice, energy equipment LCCs are mainly determined by selecting a portion of inventoried equipment to serve as a sample record for all costs incurred. The intent is to derive the economic life of the equipment-year by directly seeking its average annual cost, but this method tends to downplay maintenance, overhaul, and other cost events as “random small probability events”. This method is also incomplete for evaluating the decommissioning life of equipment whose average annual cost strictly decreases year-by-year. In this study, we analyzed the use of 75,220 KV transformers that were put into service by an energy company in 1986 as a case study (costs for this type of equipment were first recorded strictly in terms of LCC in 1986), used Isolated Forest (IF) to screen the outliers of various types of data costs, and then probability-corrected the corrected dataset with a Welbull distribution (Welbull). Then, we employed a stochastic simulation (MC) to calculate the LCC of the equipment and determined its economic lifetime (EL) and compared the results of the stochastic simulation method with those of the traditional method to provide a more reasonable explanation for the “small probability” of cost occurrences. Next, we predicted the average cost of the equipment given a use-period of 38-41-years using AHA, Bi-LSTM, and other comparative algorithms, compared the MAE, MAPE, and RMES indexes, selected the most suitable prediction model, and produced a predicted cost under the chosen method to obtain the economic life of the equipment. Finally, we compared our results with the design life of the equipment (design life being the technical life expectancy of a product based on the expectations of the manufacturer), and determined its best retirement age by comprehensively studying and judging the economic and technical benefits. The retirement age analysis was guided by by a comprehensive study of economic and technical benefits. We refer to our decommissioning life determination model as Monte Carlo -artificial hummingbird algorithm–BiLSTM–lifecycle cost model (MC-AHABi-LCC). We found that the decommissioning life obtained by MC-AHABi-LCC is closer to the actual equipment decommissioning life than that given by standard LCC and EL analysis and that our model is more accurate and scientific.

Optimal assessment of smart grid based photovoltaic cell operational parameters using simulated annealing

With the ever-increasing load demand throughout the globe, natural renewable resources integrated into the existing network architecture for sustainable energy production are gaining considerable significance. Photovoltaic (PV) generation systems is one such technique to deal with the worldwide challenge for achieving green energy and low carbon footprint while simultaneously providing emission free electrical power from solar radiations. In this paper, we consider smart grid architecture connecting the end-users and the utility power plant with solar energy sources through an effective power optimization system. Multiple performance criteria associated with solar cell operation are evaluated and analyzed using the simulated annealing algorithm. These objectives considered for optimization include the cell saturation current, photo-generated current, material band gap, cell temperature, annualized life cycle cost, fill factor and cell efficiency. The formulated optimization conditions are specified in terms of two independent variables of cell ambient temperature and cell illumination. Moreover, the adaption of distinct values of short circuit current coefficients on the light originated current is measured. Through extensive simulation experiments, two disparate annealing procedures of fast annealing and Boltzmann annealing are applied coupled with three categories of temperature update schemes, viz. exponential, logarithmic and linear.

Power control strategy and economic analysis using physics-based battery models in standalone wind–battery systems

Lithium-ion battery storage is coupled with wind power systems to address the issue of power imbalance. Operating wind turbines continuously at a maximum power point (MPP) might dump excess power based on the load demand. Manufacturer provided battery life is for lab conditions and might not replicate under real-life conditions. To address these issues, we (a) propose a power management and control strategy to avoid dumping of excess power, (b) calculate yearly capacity-fade using a physics-based electrochemical–thermal battery model for real-world operating conditions, and (c) study the financial impact of storage on the cost of wind energy generation. The power management and control strategy is designed to maintain a power balance in the system irrespective of the variations in wind speed, load demand and battery state of charge and can track the MPP or deviate from it to guarantee 0% dumping power. The formation and growth of the solid-electrolyte interface layer are modelled to calculate the battery degradation rate. Finally, seven reliability indices, including loss of energy expectation, annualised life cycle cost, and battery sufficiency, are compared for three locations with varying wind potential. The techno-economic analysis reveals that battery life and wind potential significantly impact the energy generation cost.

Integrated operation of PV assisted ground source heat pump and air source heat pump system: Performance analysis and economic optimization

The ground source heat pump (GSHP) system is widely recognized as an efficient, clean and economical renewable energy technology for buildings, and can be utilized for building cooling, heating and hot water supply. In the hot summer and cold winter zones of China, however, the total thermal load (heating load and hot water load) of GSHP systems is greater than the cooling load, which may cause a significant soil temperature drop and GSHP performance deterioration. To solve the above problems, a photovoltaic (PV) assisted GSHP and air source heat pump system (PVGSHP-ASHP) was proposed. This paper investigated the performance and feasibility of the PVGSHP-ASHP system, using a case campus building in Changsha city, China. The PVGSHP-ASHP system was firstly established by TRNSYS, and then, its operational performance was evaluated and compared with the PV assisted GSHP (PVGSHP) system and the PV assisted GSHP and electric heater (PVGSHP-EH) system. Finally, the economic optimization of the integrated operation of the PVGSHP-ASHP system was carried out with the combined simulation of TRNSYS and GenOpt. The results showed that for the PVGSHP system, the soil temperature reduced from 18.1 °C to 11.8 °C after 15 years of operation, which caused a decrease in the annual coefficient of performance of the GSHP (COPGSHP) and an increase in the annual electricity consumption. Although the PVGSHP-EH system can solve the load imbalance problem, its annual lifecycle cost (ALCC) increased by 39.3 % due to the low efficiency of the EH. The PVGSHP-ASHP system can solve the appeal problem with high annual COPGSHP and COPsys values of 3.49 kW/kW and 4.69 kW/kW, respectively, and only 71,700 kWh of the annual electricity consumption, while its ALCC was the lowest at 99,223 CNY, which was 8.1 % lower than the PVGSHP system. After optimization, the ALCC of the PVGSHP-ASHP system reached the minimized value of 91,158 CNY, with a reduction of 8.1 %. These findings can provide new ways to solve the load mismatch problem of GSHP systems while enhancing the system efficiency in the hot summer and cold winter zones of China.

Solar assisted air source heat pump systems for campus water heating in China: Economic optimization of solar fraction design

• TRNSYS software is to simulate the solar assisted air source heat pump system . • The solar fraction design values of solar water heating systems are not economically optimal in China. • Long-term economic optimization of solar fraction design values for solar assisted air source heat pump systems is performed. • An ANN-generated proxy model is to predict the most economical solar fraction design values of solar assisted air source heat pump systems. The solar assisted air source heat pump (SAASHP) system is widely used for campus water heating owing to its cost-effectiveness and cleanliness. In the current design process of the SAASHP system, the solar collector area is determined by the solar fraction ( f ). However, it is difficult to design a cost-optimal f that can comprehensively take the atmospheric factors, device model, electricity price and initial investment into consideration. To address this issue, a SAASHP system that meets the bathing hot water demand of 480 students was established by TRNSYS software, and the cost-optimal solar collector areas and f values for 12 cities across 4 different solar resource regions in China were obtained by the TRNOPT tool of TRNSYS. Moreover, the effects of SC price, electricity price, bathing hot water demand and rated COP of ASHP on cost-optimal f values were investigated by sensitivity analysis. Finally, a proxy model was generated by the artificial neural network toolbox of MATLAB to predict the cost-optimal f values for different scenarios. The results showed that compared to the economically optimized SAASHP system, the designed SAASHP system according to the national design standards for solar heating systems in China has a larger SC area value, leading to an increase in the annual life cycle cost of the system. The difference between the optimized and designed values of solar collector area and f for the SAASHP system was minor in rich resource and sub-rich resource regions but was larger in general resource and poor resource regions. The solar collector price, electricity price, bathing hot water demand and the rated COP of the SAHP were significant factors affecting the annual life cycle cost, the cost-optimal f and solar collector area of the SAASHP system. The proxy model generated by the artificial neural network toolbox of MATLAB can accurately predict the economically optimal solar collector area and f values for SAASHP systems in different cities and system factors, providing a method for the economic design of SAASHP systems in China.

Performance fluctuations and evaluation of a piston type integrated high pressure pump-energy recovery device

The development of energy recovery device (ERD) is a decisive factor for the recent prevalence of reverse osmosis (RO) technology in the seawater desalination field. Unlike broadly studied large scale ERDs, the investigations of small scale ERDs are scarcely reported, especially on the performance fluctuations under variable operating conditions. Based on an authors’ exploratory research on a piston type integrated high pressure pump-energy recovery device (HPP-ERD), further performance exploitation of the HPP-ERD is conducted. With a constructed experiment platform, the influences of inlet seawater temperature, inlet seawater salinity, as well as the operating frequency on the performance of the HPP-ERD coupled with seawater reverse osmosis (SWRO) desalination system are investigated. The temperature and salinity of the inlet seawater are numerically correlated with the specific energy consumption (SEC) of the HPP-ERD. With the correlated equation, the marine hydrological data of two observatories acquired from a national authoritative database, and the annualized life cycle cost approach incorporated, annual performance assessment is proposed for an SWRO desalination system equipped with the HPP-ERD in the light of energy and economic indices. The study results indicate that the proposed HPP-ERD is competent under various operating conditions for SWRO desalination systems.

Optimization of the solar space heating system with thermal energy storage using data-driven approach

The employment of solar space heating is a significant measure to achieve carbon neutrality. However, the design of the solar space heating system usually leads to the dilemma of a trade-off between the economic benefit and environmental protection, which cannot be solved by single-objective optimization. In this work, in order to design a solar space heating system of a bungalow equipped with radiant floor heating, multi-objective optimization of the solar collector area and the volume of the water thermal energy storage (TES) tank is performed by coupling TRNSYS simulation, machine learning and genetic algorithm (GA). Simulation for the entire winter operation of the solar space heating system is conducted in TRNSYS, and a database with various combinations of the design variables are generated, based on which an artificial neural network (ANN) is constructed to capture the mapping of the design variables and the system performance indicators. After proper training and validation, the ANN model with satisfactory forecasting precision is utilized as the objective function for optimizing the system. GA is employed for searching the solutions of the multi-objective optimizations. This data-driven approach combining ANN and GA is helpful to achieve the optimal solutions. For the solar space heating system, the CO2 emission reduction can benefit most from the enlargement of the solar collector area and the TES tank volume, while the payback period reaches its minimum when both the solar collector area and the TES tank volume are small. By comparing single-objective with multi-objective optimizations, it is found that the single-objective optimization cannot reveal the interactions among the CO2 emission reduction, annualized life cycle cost (ALCC) and payback period (PBP) and that the optimization focusing on a single objective probably leads to apparent defects and inapplicability in reality. Better balance among the CO2 emission reduction, ALCC and PBP can be achieved from the Pareto optimums of the multi-objective optimization rather than the single-objective optimization. According to the TOPSIS evaluation, the most appropriate design scheme is obtained from the multi-objective optimization and has the potential to reduce the carbon emission by 5480.6 kgCO2eq/year while the ALCC and payback period are only 33.40 ¥/(m2·year) and 3.70 years, respectively.

A novel sizing method of a standalone photovoltaic system for powering a mobile network base station using a multi-objective wind driven optimization algorithm

A new multi-objective wind driven optimization algorithm is proposed to size a standalone photovoltaic system’s components to meet the load demand for a mobile network base station at a 1% loss of load probability or less with a minimum annual total life cost. To improve the sized model’s accuracy, a long short-term memory deep learning model is utilized to forecast the hourly performance of a photovoltaic module. The long-term memory model’s performance is compared with those obtained by a linear photovoltaic model and an artificial neural network model. The comparison is carried out based on the values of normalized root mean square error, normalized mean bias error, mean absolute percentage error, and the training and testing time. Accordingly, on the values obtained for these statistical errors, the long short-term memory model outperforms better than the linear model and the artificial neural network model based. In addition, a dynamic battery model is utilized to characterize the dynamic charging and discharging process. The findings show that the optimal number of the photovoltaic array and the capacity of the storage battery required to cover the load demand of a mobile network base station are 5.4 kWp and 2640 Ah/48 V, respectively. Besides, the annual total life cycle cost for the sized photovoltaic/battery configuration is 4028.33 AUD/year. The simulation time for the proposed method is 421.25 s. To generalize the sizing results for the mobile network base stations based on Sydney weather conditions, the photovoltaic array and storage battery ratios are calculated as 0.324 and 0.223, respectively. In addition, the cost of an energy unit generated by the optimized system is 0.254 AUD/kWh. Here, the results of the proposed method have been compared with those obtained by developed and recent benchmark published methods. The comparison outcomes show the effectiveness of the proposed method in terms of providing a high availability sized system at minimum cost within less simulation time than the other considered methods.